Correlation between serum lactate and other oxygenation indices as a predictor of outcome in respiratory ICU patients

Predicting time-to-intubation after critical care admission using machine learning and cured fraction information

Intubation for mechanical ventilation (MV) is one of the most common high-risk procedures performed in Intensive Care Units (ICUs). Early prediction of intubation may have a positive impact by providing timely alerts to clinicians and consequently avoiding high-risk late intubations. In this work, we propose a new machine learning method to predict the time to intubation during the first five days of ICU admission, based on the concept of cure survival models. Our approach combines classification and survival analysis, to effectively accommodate the fraction of patients not at risk of intubation, and provide a better estimate of time to intubation, for patients at risk. We tested our approach and compared it to other predictive models on a dataset collected from a secondary care hospital (AZ Groeninge, Kortrijk, Belgium) from 2015 to 2021, consisting of 3425 ICU stays. Furthermore, we utilised SHAP for feature importance analysis, extracting key insights into the relative significance of variables such as vital signs, blood gases, and patient characteristics in predicting intubation in ICU settings. The results corroborate that our approach improves the prediction of time to intubation in critically ill patients, by using routinely collected data within the first hours of admission in the ICU. Early warning of the need for intubation may be used to help clinicians predict the risk of intubation and rank patients according to their expected time to intubation.

Endogenous Carboxyhemoglobin Level Variation in COVID-19 and Bacterial Sepsis: A Novel Approach?

Background: The increased production of carbon monoxide (CO) in sepsis has been proven, but the blood level variations of carboxyhemoglobin (COHb) as a potential evolutionary parameter of COVID-19 and sepsis/septic shock have yet to be determined. This study aims to evaluate the serum level variation of COHb as a potential evolutionary parameter in COVID-19 critically ill patients and in bacterial sepsis. Materials and method: A prospective and observational study was conducted on two groups of patients: the bacterial sepsis group (n = 52) and the COVID-19 group (n = 52). We followed paraclinical parameters on Day 1 (D1) and Day 5 (D5) of sepsis/ICU admission for COVID-19 patients. Results: D1 of sepsis: statistically significant positive correlations between: COHb values and serum lactate (p = 0.024, r = 0.316), and total bilirubin (p = 0.01, r = 0.359). In D5 of sepsis: a statistically significant positive correlations between: COHb values and procalcitonin (PCT) (p = 0.038, r = 0.402), and total bilirubin (p = 0.023, r = 0.319). D1 of COVID-19 group: COHb levels were statistically significantly positively correlated with C-reactive protein CRP values (p = 0.003, r = 0.407) and with PCT values (p = 0.022, r = 0.324) and statistically significantly negatively correlated with serum lactate values (p = 0.038, r = −0.285). Conclusion: COHb variation could provide rapid information about the outcome of bacterial sepsis/septic shock, having the advantages of a favorable cost-effectiveness ratio, and availability as a point-of-care test.

Is Serum Lactate a Good Predictor of Mortality in Children Aged 2 Months to 5 Years With Pneumonia in Central Vietnam

Pneumonia is a major cause of morbidity and mortality in children globally. Lactate, a product of anaerobic cellular metabolism, has been used as an indicator of poor tissue oxygenation and cellular hypoxia. Our objective was to determine whether serum lactate concentration at hospital admission predicted mortality in children aged 2 months to 5 years with pneumonia. Two hundred and eighty-one pediatric patients admitted to the Department of Pediatrics of a provincial hospital with WHO-defined pneumonia and severe pneumonia were included; of whom, 8 died during hospital stay. The median serum lactate concentration was 4.8 mmol/l (IQR 2.6-6.9) among children who died and 3.6 mmol/l (IQR 2.8-4.3) among children who survived (P > .05); 4.1 mmol/l (IQR 2.7-4.7) among children with severe pneumonia and 3.5 mmol/l (IQR 2.8-4.3) among children with pneumonia (P > .05). Serum lactate concentration had a low value in predicting pneumonia-related mortality (AUC 0.68, 95% CI 0.62-0.73); and the concentration cut-off of >4.06 mmol/l had the best sensitivity and specificity (75% and 68.9%, respectively) with a 2.4-fold risk of death (LR+ 2.4; 95% CI 1.6-3.7). Although hyperlactatemia was associated with severity and mortality in children 2 months to 5 years of age with pneumonia, its benefit was unclear.

On-demand lactate monitoring towards assessing physiological responses in sedentary populations

Identification of diseases in sedentary populations on a timely basis before reaching a critical stage is a continuing challenge faced by emergency care centers. Lactate is a key biomarker for monitoring restricted oxygen supply essential for assessing the physiological responses of the user for clinical diagnostics. The novelty of this work is the development of a non-invasive, mediator-free, stick and remove biosensor for the on-demand measurement of lactate in passive sweat targeted towards sedentary populations. The conformable interface of the biosensors with skin can be engineered to extract relevant biochemical signals and quantify the in situ sweat biomarker levels. In this work, we demonstrate a highly sensitive and specific on-demand biosensor with a fabricated hybrid nanotextured Au/ZnO electrode stack embedded within a flexible nanoporous material to capture the temporal dynamics of passive sweat lactate. The biosensor exhibits a lactate specific response in human sweat with a 1 mM lower limit of detection and a wide dynamic detection range of 1-100 mM (R² = 0.98). The proposed biosensor has a sensitivity of 8.3% mM^-1 while selectivity studies reveal negative interactions with non-specific molecules. The sensor stability studies showed an ∼30% degradation in the lactate biosensing response over a 4-day duration when stored at 4 °C. Non-faradaic electrochemical spectroscopy is employed as the detection modality to quantify the enzymatic catalysis of sweat lactate at the electrode-sweat interface. Spectroscopic characterization techniques such as XPS, ATR-FTIR, and zeta potential measurements confirm the enzymatic assay binding efficacy on a qualitative scale.

Does lactate clearance prognosticates outcomes in ECMO therapy: a retrospective observational study

Background

ECMO support is a final treatment modality for patients in the refractory cardiogenic arrest and postcardiotomy cardiogenic shock with an utmost importance. Eventhough it is linked to high mortality, its usage gains popularity worldwide. We assessed the fluctuation of lactate levels and the clearance of lactate during the ECMO therapy and its prognostic role on mortality.

Methods

Data were gathered on all patients receiving ECMO therapy longer than 48 h between January 2015 and December 2017 retrospectively. Blood lactate had been recorded before ECMO implantation and at specific time points during ECMO support as a routine procedure. In this study, the Lactate clearance at specific time points (Lactate clearance-1) and the duration that lactate cleared more than 10% of the initial lactate level (Lactate clearance-2) was measured. Statistical analysis included Mann Whitney U-test and ROC-curves to predict 30-day mortality.

Results

Fourty-eight patients underwent ECMO therapy for refractory cardiogenic shock resulting in 70.8% mortality. The lactate levels before and after ECMO therapy as well as the dynamic changes were significantly correlated with mortality variable. With AUC calculation, LC-2 has a strong discrimination (AUC = 0.97) on 30-day survivors and nonsurvivors. LAE-LBE (AUC = 0.785), L48-LBE (AUC = 0.706) showed moderate predictive power on 30-day mortality.

Conclusions

Changes in lactate levels after ECMO implantation is an important tool to assess effective circulatory support and it is found superior to single lactate measurements as a prognostic sign of mortality in our study. Based on our results, an early insertion of ECMO before lactate gets high was suggested. Serial changes on lactate levels and calculation of its clearance may be superior to single lactate on both effective circulatory support and as prognostic prediction. LC-2 showed a strong discrimination on 30-day mortality.